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Cloudera CDP Data Engineer - Certification Sample Questions:

1. When would it be advantageous to use both partitioning and bucketing on a Hive table?

A) When dealing with large datasets that require efficient querying and data sampling
B) When data needs to be stored in a single file for archival purposes
C) When data security is a primary concern
D) When managing small datasets to reduce complexity

2. When creating a partitioned table in Hive, what does the clause PARTITIONED BY specify?

A) The replication factor for the HDFS data blocks
B) The column(s) used to divide the table into partitions
C) The default file format for data storage
D) The compression algorithm used for data storage

3. You need to filter data from a Hive table based on a specific date range. Which approach would be most efficient and maintainable?

A) Implement a custom filter function in Spark to process each row individually
B) Use Spark SQL functions like filter with a date comparison expression
C) Convert the Hive table to a temporary table and then use Spark SQL filtering
D) Leverage HiveQL's built-in filtering capabilities with a WHERE clause

4. You need to read data from a Hive table into a Spark DataFrame. Which approach would be the most efficient?

A) Leverage Spark SQL capabilities with SELECT FROM table_name
B) Use the spark.read.parquet("/path/to/hive/table") method directly
C) Convert the Hive table to a managed table and then use spark.read.table("table_name")
D) Use the FROM table_name") method

5. When leveraging caching in Spark, which scenario illustrates the use of the MEMORY ONLY SER storage level most effectively?

A) Caching small, static datasets used in lookup operations.
B) Caching datasets that require fast, sequential access without the need for serialization.
C) Caching large datasets that do not fit into memory when stored in deserialized form.
D) Caching datasets that are frequently accessed and modified.

Solutions: